The chapter “Three Kinds of Models” from the book “Simulation and Similarity” discussed different types
of scientific models, including mathematical models, concrete models, and computational models.
Computational models are rapidly becoming the primary form of scientific models. Mathematical models
still dominate the attention of much of theoretical science, as well as the philosophers who study it. All
three models consist of an interpreted structure that can be used to represent a real or imagined
phenomenon. The paradigm examples of these three models are: The Bay model is a concrete tub with a
set of pumps and appurtenances, the Lotka—Volterra model is a set of points in a mathematically
constructed space, and the Schelling model is a set of states and transitions. Later, the chapter also
discussed how to expand and shrink the list of model types. In summary, every model is composed of a
structure along with an interpretation of that structure.
The other reading “Philosophy and Modeling and Simulation” by Tolk et al. (2023) discussed what type of
knowledge can be acquired from simulations. When a user of the simulation accepted the results at face
value, the validity context became more crucial than ever. More than ever, there are connections
between modeling, simulation, and scientific methods. The study explored the use of simulation as a
platform for conducting experiments. Simulated results can be used by scientists to assess a hypothesis'
viability. Further, the issue has been discussed in the context of epistemology- the area of philosophy
that deals with the problem of how to learn new things in a particular field. In conclusion, while
modeling and the scientific method have long been closely linked, the development of computational
power has made it possible to use simulation to advance computational sciences, enhancing their ability
to produce new insights and instruct students through the use of immersive visualization of underlying
dynamics.
To sum up, these readings set the base for understanding the importance of modeling and simulation in
the scientific community. Every researcher, working with a large chunk of data at least once gets stuck in
a situation when they have to consider the use of these models or work with the models. Understanding
the philosophy behind these models increase the perspective to critically access our own work and strive
to make these model work as close to reality as possible.